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Personalized Federated Learning

The federated learning setup presents numerous challenges including data heterogeneity (differences in data distribution), device heterogeneity (in terms of computation capabilities, network connection, etc.), and communication efficiency. Especially data heterogeneity makes it hard to learn a single shared global model that applies to all clients. To overcome these issues, Personalized Federated Learning (PFL) aims to personalize the global model for each client in the federation.

Papers

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Showing 41–50 of 311 papers

TitleStatusHype
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning—0
Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised LearningCode3
Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series DataCode0
Optimizing Personalized Federated Learning through Adaptive Layer-Wise LearningCode1
FedAH: Aggregated Head for Personalized Federated LearningCode0
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal TransportCode0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
Personalized Federated Learning for Cross-view Geo-localization—0
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation—0
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